Implementation of a mortality prediction rule for real-time decision making: Feasibility and validity.

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Implementation of a mortality prediction rule for real-time decision making: Feasibility and validity.

J Hosp Med. 2014 Aug 11;

Authors: Cowen ME, Czerwinski JL, Posa PJ, Van Hoek E, Mattimore J, Halasyamani LK, Strawderman RL

BACKGROUND: A previously published, retrospectively derived prediction rule for death within 30 days of hospital admission has the potential to launch parallel interdisciplinary team activities. Whether or not patient care improves will depend on the validity of prospectively generated predictions, and the feasibility of generating them on demand for a critical proportion of inpatients.
OBJECTIVE: To determine the feasibility of generating mortality predictions on admission and to validate their accuracy using the scoring weights of the retrospective rule.
DESIGN: Prospective, sequential cohort.
SETTING: Large, tertiary care, community hospital in the Midwestern United States PATIENTS: Adult patients admitted from the emergency department or scheduled for elective surgery RESULTS: Mortality predictions were generated on demand at the beginning of the hospitalization for 9312 (92.9%) out of a possible 10,027 cases. The area under the receiver operating curve for 30-day mortality was 0.850 (95% confidence interval: 0.833-0.866), indicating very good to excellent discrimination. The prospectively generated 30-day mortality risk had a strong association with the receipt of palliative care by hospital discharge, in-hospital mortality, and 180-day mortality, a fair association with the risk for 30-day readmissions and unplanned transfers to intensive care, and weak associations with receipt of intensive unit care ever within the hospitalization or the development of a new diagnosis that was not present on admission (ie, complication).
CONCLUSIONS: Important prognostic information is feasible to obtain in a real-time, single-assessment process for a sizeable proportion of hospitalized patients. Journal of Hospital Medicine 2014. © 2014 Society of Hospital Medicine.

PMID: 25111067 [PubMed - as supplied by publisher]

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